I'm in the camp of the rationales of Go's design being controversial. But the actual design is not bad IMO. For what Go wants to be (a modern C), it is not a bad design. But who wants a modern C? I don't. But if Go were available in the 90s, I would have jumped at the opportunity to write programs in it.
> Python is decidedly not dying... it's still the dominating game in town for ML and data science. I'm surprised anybody would posit that Python were dying. Perl definitely feels like it should be in Ruby's camp.
I didn't say that I would consider Python a dying language. But considering Ruby dying and Perl don't, is very weird. OTOH Ruby and Python aren't gaining any new usecases (but also aren't losing anything rapidly AFAIK) whereas Perl ... has any left besides very legacy code?
Putting Perl and Python into the same group and Ruby in another is certainly an interesting choice.
Sure and there's this weird thing where Go felt marketed as a C replacement and then Rust existed and it was all "just kidding we meant services" and boy do I think Go is poorly designed for services. It has a lot of characteristics that make it appealing - fast compiler and execution speed, low resource usage... but I don't see how pointers, a half-baked type system and a totally undesigned error system are good design for where Go is currently winning.
> But considering Ruby dying and Perl don't, is very weird.
Agreed for sure, I think Ruby and Perl probably have similar trajectories based on current trends in languages. Python's staying power IMO is really only due to the data science/ML trends and I frankly think it's a pretty poor language and ecosystem if you can get away with using anything else. I think Python WOULD be losing ground if not for its popularity in these very large niches.
Python will gain usecases as a meta-language as the ability to automatically wrap lower abstraction libaries improves. I see Julia eating much of Python's lunch, then python abstracting its way over julia, just because it can.
A big problem for Python as a glue language is interproceedural optimizations. In many important high performance applications, you really need the compiler to be able to make optimizations that cross function barriers, often barriers between user-written and developer-written functions.
e.g. in differential equation solving, you can have a very fast integrator, and your user can have a very fast integrand, but if you don't have a compiler that's able to see and reason about both simultaneously, you're in trouble.
This is why slapping Numba on a differential equation and sticking it in scipy integrators isn't even close to julia DifferentialEquation solvers. This is a very hard problem to solve in an ecosystem where there's so many different siloed compilers used by different Python packages. You'll always end up paying a performance price of the context switch.
In julia, most things are written in pure julia, so the same compiler is seeing the entire program top to bottom. It's very nice.
Furthermore, Julia has much power powerful metaprogramming facilities than Python does. It'll eat Python's lunch on that front too.
On the other hand Julia is a terribly designed language in some respects. Compare python error messages to Julias. It has all the problems of c++ templates baked in from the start. And latency is a real issue for now. I am always shocked when I drop back to Python for one task or another how responsive it feels.
I think the latter might see a technical solution some time, e.g. by running code in the interpreter and swapping in jotted functions as they become available.
The former it seems might have to wait for Julia 2.0 to start addressing...
> Furthermore, Julia has much power powerful metaprogramming facilities than Python does.
Until someone writes a way to import julia code directly into python/autogenerate it from python. Anyone who says "well then you're not really writing python" is missing the whole point: writing python is all about exactly this sort of tip-of-the-abstraction-iceberg trickery.
Sure, you can call python from Julia as well. So it boils down to "which is the better glue language?" This isn't a competitive thing, it's just literally what Python is meant to do.
As in, a minimalist language aesthetic or fast low-level non-gc? Golang only has the aesthetics, Zig has both.
Python is the only language I've had to deploy that makes me understand why anybody would want to use containers.
Spending a bit of time with Rust's tooling, static analysis, and "if it compiles it's pretty likely to work" (at least way more so than with Python) definitely emphasizes a lot of Python's flaws.
Did you know that pip won't even resolve dependencies, and will break your programs silently? I certainly didn't, because Python wasn't my first language and therefore I assumed that the package manager will handle dependencies.
Like seriously, when a language designed by statisticians for statisticians (R) has a better packaging story than everyone's second favourite language, something has gone horribly wrong.
I almost used Perl as an example of Python's inverse - a crappy language wrapped in a good packaging system. For its time CPAN was pretty great. All of the things today that people are likely to hold up as being better drew inspiration from it.
> which is much older than Python
Perl 1987, Python 1989. So yes it's older, but much older?
I'm just so annoyed by Python's (lack of a) packaging system that it makes me prone to hyperbole. It's been frustrating me at work all week, and I suspect that it will be a low-grade annoyance for me in my career for the next few years, as I'm a Data Scientist and hence need to deal with Python a lot.
Go's design is at the very least controversial and considered poor by a vocal subset of the hackernews and programming community.
Python is decidedly not dying... it's still the dominating game in town for ML and data science. I'm surprised anybody would posit that Python were dying. Perl definitely feels like it should be in Ruby's camp.